Tran Minh Ngoc, PhD
College of Engineering and Computer Science
Affiliate Faculty
Professor - Data Science
Biography
Dr. Tran Minh Ngoc is Full Professor of Business Analytics at the University of Sydney Business School. He earned his BSc and MSc in Mathematics from Vietnam National University, Hanoi, before completing a PhD in Statistics at the National University of Singapore in 2012.
His research spans statistical methodology and applied statistics. On the methodological side, he develops Bayesian computation and machine learning techniques, with a particular emphasis on variational Bayes and the integration of emerging quantum computation methods into data analysis. In applied work, he leverages modern statistical approaches to advance studies in cognitive science, consumer behaviour and financial econometrics.
Minh Ngoc’s contributions have been recognised nationally and internationally, with publications in leading statistical journals and conferences. His research has attracted over AUD 5 million in funding, including three competitive ARC grants, and he is a sought after speaker at both national and international meetings. As an enthusiastic educator, Minh Ngoc adopts a research led, student focused teaching style that consistently earns outstanding feedback.
• Statistical methodologies
• Quantum comuting for statistics
• Financial Econometrics
• Cognitive Science
• Predictive Analytics
• Bayesian analysis
• Statistical Machine Learning
• Mathematical Statistics
• Data Science for Business
· Godichon-Baggioni, Nguyen and Tran (2025). Natural Gradient Variational Bayes without Fisher Matrix Analytic Calculation and Its Inversion. Journal of the American Statistical Association (in press).
· Dao, V., Gunawan, D., Kohn, R., Tran, M., Hawkins, G., Brown, S. (2025). Bayesian Inference for Evidence Accumulation Models with Regressors. Psychological Methods (in press).
· Lopatnikova, A., Tran, M., Sisson, S. (2024). An Introduction to Quantum Computing for Statisticians and Data Scientists. Foundations of Data Science, 6(3), 278-307.
· Dao, V., Gunawan, D., Tran, M., Kohn, R., Hawkins, G., Brown, S. (2024). Efficient selection between hierarchical cognitive models: Cross-validation with variational Bayes. Psychological Methods, 29(1), 219-241.
· Nguyen, N., Tran, M., Gunawan, D., Kohn, R. (2023). A Statistical Recurrent Stochastic Volatility Model for Stock Markets. Journal of Business and Economic Statistics, 41(2), 414-428.
· Nguyen, Tran and Kohn (2022). Recurrent conditional heteroskedasticity. Journal of Applied Econometrics, 37(5), 1031-1054.
· Gunawan, Hawkins, Tran, Kohn and Brown (2022). Time-evolving psychological processes over repeated decisions. Psychological Review, 129(3), 438-456.
· Tran, Nguyen and Nguyen (2021). Variational Bayes on manifolds. Statistics and Computing, 31(6), 1-17.
· Salomone, Quiroz, Kohn, Villani and Tran (2020). Spectral Subsampling MCMC for Stationary Time Series. ICML 2020. · K Dang, M Quiroz, R Kohn, M Tran and M Villani (2019). Hamiltonian Monte Carlo with Energy Conserving Subsampling. Journal of Machine Learning Research, 20, 1-31.
· D. Gunawan, S. Brown, R. Kohn, M-N. Tran (2019). New Estimation Approaches for the Linear Ballistic Accumulator Model. Journal of Mathematical Psychology, 96, 102368.
· Tran, Nguyen, Nott and Kohn (2019) Bayesian Deep Net GLM and GLMM. Journal of Computational and Graphical Statistics, 29(1), 97-113.
· M Quiroz, M Villani, R Kohn and M Tran (2018). Speeding up MCMC by efficient data subsampling, Journal of the American Statistical Association, 114(526), 831-843.
